Systems and methods for determining effectiveness of network segmentation policies

ABSTRACT

Disclosed herein are methods, systems, and non-transitory computer-readable storage media for scoring network segmentation policies in order to determine their effectiveness before, during and after enforcement. In one aspect, a method includes identifying one or more applications within an enterprise network; identifying at least one network security policy in association with the one or more applications within the enterprise network; determining a score of the network security policy based on information corresponding to exposure of each of the one or more applications within the enterprise network; and executing the network security policy based on the score.

TECHNICAL FIELD

The subject matter of this disclosure relates in general to the field ofcomputer networks, and more specifically to determining effectiveness ofnetwork policies before, during and after implementation of the networkpolicies in the network.

BACKGROUND

With expansion of enterprise networks and their applicability, variousnetwork security policies may be applied to network nodes and workloadsto ensure safety and security of the entire enterprise network. Forexample, network operators must develop and enforce network segmentationpolicies which govern who can access any given service or workload,which devices can access specific services, etc.

However, there is not objective and standard approach formeasuring/predicting effectiveness of such network segmentation policieson application performance within the network.

BRIEF DESCRIPTION OF THE FIGURES

In order to describe the manner in which the above-recited and otheradvantages and features of the disclosure can be obtained, a moreparticular description of the principles briefly described above will berendered by reference to specific embodiments that are illustrated inthe appended drawings. Understanding that these drawings depict onlyembodiments of the disclosure and are not therefore to be considered tobe limiting of its scope, the principles herein are described andexplained with additional specificity and detail through the use of theaccompanying drawings in which:

FIG. 1 illustrates an example of a network traffic monitoring system,according to one aspect of the present disclosure;

FIG. 2 illustrates an example of a network environment, according to oneaspect of the present disclosure;

FIG. 3 illustrates an example of a data pipeline for generating networkinsights based on collected network information, according to one aspectof the present disclosure;

FIG. 4 describes a process of determining an effectiveness of asegmentation policy, according to one aspect of the present disclosure;

FIG. 5 describes a process for determining policy effectiveness score ofFIG. 4, according to one aspect of the present disclosure;

FIG. 6 is an example network segmentation policy, according to an aspectof the present disclosure;

FIG. 7 illustrates a result of determining policy effectiveness score ina tabular form, according to one aspect of the present disclosure; and

FIG. 8 illustrates an example computing system, according to one aspectof the present disclosure.

DESCRIPTION OF EXAMPLE EMBODIMENTS

Various embodiments of the disclosure are discussed in detail below.While specific implementations are discussed, it should be understoodthat this is done for illustration purposes only. A person skilled inthe relevant art will recognize that other components and configurationsmay be used without parting from the spirit and scope of the disclosure.Thus, the following description and drawings are illustrative and arenot to be construed as limiting. Numerous specific details are describedto provide a thorough understanding of the disclosure. However, incertain instances, well-known or conventional details are not describedin order to avoid obscuring the description. References to one or anembodiment in the present disclosure can be references to the sameembodiment or any embodiment; and, such references mean at least one ofthe embodiments.

Reference to “one embodiment” or “an embodiment” means that a particularfeature, structure, or characteristic described in connection with theembodiment is included in at least one embodiment of the disclosure. Theappearances of the phrase “in one embodiment” in various places in thespecification are not necessarily all referring to the same embodiment,nor are separate or alternative embodiments mutually exclusive of otherembodiments. Moreover, various features are described which may beexhibited by some embodiments and not by others.

The terms used in this specification generally have their ordinarymeanings in the art, within the context of the disclosure, and in thespecific context where each term is used. Alternative language andsynonyms may be used for any one or more of the terms discussed herein,and no special significance should be placed upon whether or not a termis elaborated or discussed herein. In some cases, synonyms for certainterms are provided. A recital of one or more synonyms does not excludethe use of other synonyms. The use of examples anywhere in thisspecification including examples of any terms discussed herein isillustrative only, and is not intended to further limit the scope andmeaning of the disclosure or of any example term. Likewise, thedisclosure is not limited to various embodiments given in thisspecification.

Without intent to limit the scope of the disclosure, examples ofinstruments, apparatus, methods and their related results according tothe embodiments of the present disclosure are given below. Note thattitles or subtitles may be used in the examples for convenience of areader, which in no way should limit the scope of the disclosure. Unlessotherwise defined, technical and scientific terms used herein have themeaning as commonly understood by one of ordinary skill in the art towhich this disclosure pertains. In the case of conflict, the presentdocument, including definitions will control.

Additional features and advantages of the disclosure will be set forthin the description which follows, and in part will be obvious from thedescription, or can be learned by practice of the herein disclosedprinciples. The features and advantages of the disclosure can berealized and obtained by means of the instruments and combinationsparticularly pointed out in the appended claims. These and otherfeatures of the disclosure will become more fully apparent from thefollowing description and appended claims, or can be learned by thepractice of the principles set forth herein.

Overview

Disclosed herein are methods, systems, and non-transitorycomputer-readable storage media for scoring network segmentationpolicies in order to determine their effectiveness before, during andafter enforcement. Such scoring system provides an objective measurementof how effective any given network segmentation/security policy is. Themeasurement can then be used to modify/adjust segmentation and securitypolicies in the network

In one aspect, a method includes identifying one or more applicationswithin an enterprise network; identifying at least one network securitypolicy in association with the one or more applications within theenterprise network; determining a score of the network security policybased on information corresponding to exposure of each of the one ormore applications within the enterprise network; and executing thenetwork security policy based on the score.

In another aspect, the method further includes receiving a request fordetermining network security policy scores.

In another aspect, identifying the one or more applications includesdetermining an application hierarchy and membership for each of the oneor more applications in the network.

In another aspect, identifying the network security policy includesperforming an application dependency mapping for the one or moreapplications.

In another aspect, determining the score for the network security policyincludes identifying the information corresponding to the exposure ofthe one or more applications.

In another aspect, the information includes providers, consumers andservices associated with each of the one or more applications.

In another aspect, executing the network security policy includes one ofmodifying, replacing or maintaining a current version of the networksecurity policy.

In one aspect, a system includes one or more processors and anon-transitory computer-readable storage medium including instructionsstored thereon which, when executed by the one or more processors, causethe one or more processors to identify one or more applications withinan enterprise network; identify at least one network security policy inassociation with the one or more applications within the enterprisenetwork; determine a score of the network security policy based oninformation corresponding to exposure of each of the one or moreapplications within the enterprise network; and execute the networksecurity policy based on the score.

In one aspect, a non-transitory computer-readable storage medium hasstored thereon instructions which, when executed by at least oneprocessor, cause the at least one processor to identify one or moreapplications within an enterprise network; identify at least one networksecurity policy in association with the one or more applications withinthe enterprise network; determine a score of the network security policybased on information corresponding to exposure of each of the one ormore applications within the enterprise network; and execute the networksecurity policy based on the score.

Description

Various embodiments of the disclosure are discussed in detail below.While specific implementations are discussed, it should be understoodthat this is done for illustrative purposes only. A person skilled inthe relevant art will recognize that other components and configurationsmay be used without departing from the spirit and scope of thedisclosure.

As noted above, there is currently no objective and standardizedapproach and system for measuring/predicting effectiveness of networksegmentation and security policies. The present disclosure is directedto providing such scoring system measuring the effectiveness of aderived or enforced network segmentation policy. Segmentationeffectiveness may be defined as a measure of effectiveness of aparticular policy given the exposure of an application to hosts withinthe infrastructure of an enterprise network and/or to resources externalto the enterprise network but accessible via the enterprise network(e.g., external internet hosts). Such measurement may be indicative ofhow well a given policy is satisfying the determined objective ofpreventing unauthorized traffic or hosts from accessing an applicationor a set of services/workloads executed in association with anapplication. This objective measurement improves efficiency in ensuringnetwork security by allowing policies to be measured beforeimplementation and adjusted if need be and/or to reliably adjustexisting and currently implemented network segmentation policies in thenetwork.

As will be described below, effectiveness score may be determined basedon a number of variables such as source exposure risk, destinationexposure risk and security posture. A source exposure risk may beindicative of a total number of hosts and workloads accessing a givenapplication. A destination exposure risk may be indicative of a totalnumber of hosts and workloads providing services for a givenapplication. Security posture may be factor of overall consumers andproviders that form/constitute a given application.

The disclosure begins with an initial discussion of systems andtechnologies for monitoring network activity. A description of examplesystems, methods, and environments for this monitoring technology willbe discussed in FIGS. 1 through 3. The discussion will then continuewith methods, systems, and non-transitory computer-readable media fordetermining effectiveness of network security and segmentation policieswith reference to FIGS. 4-7. The disclosure concludes with a descriptionof an example computing system, described in FIG. 8, which can beutilized as components of systems and environments described withreference to FIGS. 1-7.

The disclosure now turns to an initial discussion of example systems andtechnologies for monitoring network activity.

Sensors deployed in a network can be used to gather network informationrelated to network traffic of nodes operating in the network and processinformation for nodes and applications running in the network. Gatherednetwork information can be analyzed to provide insights into theoperation of the nodes in the network, otherwise referred to asanalytics/telemetry data. In particular, discovered applications orinventories, application dependencies, policies, efficiencies, resourceand bandwidth usage, and network flows can be determined for the networkusing the network traffic data. For example, an analytics engine can beconfigured to automate discovery of applications running in the network,map the applications' interdependencies, or generate a set of proposednetwork policies for implementation.

The analytics engine can monitor network information, processinformation, and other relevant information of traffic passing throughthe network using a sensor network that provides multiple perspectivesfor the traffic. The sensor network can include sensors for networkingdevices (e.g., routers, switches, network appliances), physical servers,hypervisors or shared kernels, and virtual partitions (e.g., VMs orcontainers), and other network elements. The analytics engine cananalyze the network information, process information, and otherpertinent information to determine various network insights.

Referring now to the drawings, FIG. 1 illustrates an example of anetwork traffic monitoring system, according to one aspect of thepresent disclosure. The network traffic monitoring system 100 caninclude a configuration manager 102, sensors 104, a collector module106, a data mover module 108, an analytics engine 110, and apresentation module 112. In FIG. 1, the analytics engine 110 is alsoshown in communication with out-of-band data sources 114, third partydata sources 116, and a network controller 118.

The configuration manager 102 can be used to provision and maintain thesensors 104, including installing sensor software or firmware in variousnodes of a network, configuring the sensors 104, updating the sensorsoftware or firmware, among other sensor management tasks. For example,the sensors 104 can be implemented as virtual partition images (e.g.,virtual machine (VM) images or container images), and the configurationmanager 102 can distribute the images to host machines. In general, avirtual partition may be an instance of a VM, container, sandbox, orother isolated software environment. The software environment mayinclude an operating system and application software. For softwarerunning within a virtual partition, the virtual partition may appear tobe, for example, one of many servers or one of many operating systemsexecuted on a single physical server. The configuration manager 102 caninstantiate a new virtual partition or migrate an existing partition toa different physical server. The configuration manager 102 can also beused to configure the new or migrated sensor.

The configuration manager 102 can monitor the health of the sensors 104.For example, the configuration manager 102 may request status updatesand/or receive heartbeat messages, initiate performance tests, generatehealth checks, and perform other health monitoring tasks. In someembodiments, the configuration manager 102 can also authenticate thesensors 104. For instance, the sensors 104 can be assigned a uniqueidentifier, such as by using a one-way hash function of a sensor's basicinput/out system (BIOS) universally unique identifier (UUID) and asecret key stored by the configuration image manager 102. The UUID canbe a large number that may be difficult for a malicious sensor or otherdevice or component to guess. In some embodiments, the configurationmanager 102 can keep the sensors 104 up to date by installing the latestversions of sensor software and/or applying patches. The configurationmanager 102 can obtain these updates automatically from a local sourceor the Internet.

The sensors 104 can reside on various nodes of a network, such as avirtual partition (e.g., VM or container) 120; a hypervisor or sharedkernel managing one or more virtual partitions and/or physical servers122, an application-specific integrated circuit (ASIC) 124 of a switch,router, gateway, or other networking device, or a packet capture (pcap)126 appliance (e.g., a standalone packet monitor, a device connected toa network devices monitoring port, a device connected in series along amain trunk of a datacenter, or similar device), or other element of anetwork. The sensors 104 can monitor network traffic between nodes, andsend network traffic data and corresponding data (e.g., host data,process data, user data, etc.) to the collectors 106 for storage. Forexample, the sensors 104 can sniff packets being sent over its hosts'physical or virtual network interface card (NIC), or individualprocesses can be configured to report network traffic and correspondingdata to the sensors 104. Incorporating the sensors 104 on multiple nodesand within multiple partitions of some nodes of the network can providefor robust capture of network traffic and corresponding data from eachhop of data transmission. In some embodiments, each node of the network(e.g., VM, container, or other virtual partition 120, hypervisor, sharedkernel, or physical server 122, ASIC 124, pcap 126, etc.) includes arespective sensor 104. However, it should be understood that varioussoftware and hardware configurations can be used to implement the sensornetwork 104.

As the sensors 104 capture communications and corresponding data, theymay continuously send network traffic data to the collectors 106. Thenetwork traffic data can include metadata relating to a packet, acollection of packets, a flow, a bidirectional flow, a group of flows, asession, or a network communication of another granularity. That is, thenetwork traffic data can generally include any information describingcommunication on all layers of the Open Systems Interconnection (OSI)model. For example, the network traffic data can includesource/destination MAC address, source/destination IP address, protocol,port number, etc. In some embodiments, the network traffic data can alsoinclude summaries of network activity or other network statistics suchas number of packets, number of bytes, number of flows, bandwidth usage,response time, latency, packet loss, jitter, and other networkstatistics.

The sensors 104 can also determine additional data for each session,bidirectional flow, flow, packet, or other more granular or lessgranular network communication. The additional data can include hostand/or endpoint information, virtual partition information, sensorinformation, process information, user information, tenant information,application information, network topology, application dependencymapping, cluster information, or other information corresponding to eachflow.

In some embodiments, the sensors 104 can perform some preprocessing ofthe network traffic and corresponding data before sending the data tothe collectors 106. For example, the sensors 104 can remove extraneousor duplicative data or they can create summaries of the data (e.g.,latency, number of packets per flow, number of bytes per flow, number offlows, etc.). In some embodiments, the sensors 104 can be configured toonly capture certain types of network information and disregard therest. In some embodiments, the sensors 104 can be configured to captureonly a representative sample of packets (e.g., every 1,000th packet orother suitable sample rate) and corresponding data.

Since the sensors 104 may be located throughout the network, networktraffic and corresponding data can be collected from multiple vantagepoints or multiple perspectives in the network to provide a morecomprehensive view of network behavior. The capture of network trafficand corresponding data from multiple perspectives rather than just at asingle sensor located in the data path or in communication with acomponent in the data path, allows the data to be correlated from thevarious data sources, which may be used as additional data points by theanalytics engine 110. Further, collecting network traffic andcorresponding data from multiple points of view ensures more accuratedata is captured. For example, other types of sensor networks may belimited to sensors running on external-facing network devices (e.g.,routers, switches, network appliances, etc.) such that east-westtraffic, including VM-to-VM or container-to-container traffic on a samehost, may not be monitored. In addition, packets that are dropped beforetraversing a network device or packets containing errors may not beaccurately monitored by other types of sensor networks. The sensornetwork 104 of various embodiments substantially mitigates or eliminatesthese issues altogether by locating sensors at multiple points ofpotential failure. Moreover, the network traffic monitoring system 100can verify multiple instances of data for a flow (e.g., source endpointflow data, network device flow data, and endpoint flow data) against oneanother.

In some embodiments, the network traffic monitoring system 100 canassess a degree of accuracy of flow data sets from multiple sensors andutilize a flow data set from a single sensor determined to be the mostaccurate and/or complete. The degree of accuracy can be based on factorssuch as network topology (e.g., a sensor closer to the source may bemore likely to be more accurate than a sensor closer to thedestination), a state of a sensor or a node hosting the sensor (e.g., acompromised sensor/node may have less accurate flow data than anuncompromised sensor/node), or flow data volume (e.g., a sensorcapturing a greater number of packets for a flow may be more accuratethan a sensor capturing a smaller number of packets).

In some embodiments, the network traffic monitoring system 100 canassemble the most accurate flow data set and corresponding data frommultiple sensors. For instance, a first sensor along a data path maycapture data for a first packet of a flow but may be missing data for asecond packet of the flow while the situation is reversed for a secondsensor along the data path. The network traffic monitoring system 100can assemble data for the flow from the first packet captured by thefirst sensor and the second packet captured by the second sensor.

As discussed, the sensors 104 can send network traffic and correspondingdata to the collectors 106. In some embodiments, each sensor can beassigned to a primary collector and a secondary collector as part of ahigh availability scheme. If the primary collector fails orcommunications between the sensor and the primary collector are nototherwise possible, a sensor can send its network traffic andcorresponding data to the secondary collector. In other embodiments, thesensors 104 are not assigned specific collectors but the network trafficmonitoring system 100 can determine an optimal collector for receivingthe network traffic and corresponding data through a discovery process.In such embodiments, a sensor can change where it sends it networktraffic and corresponding data if its environments changes, such as if adefault collector fails or if the sensor is migrated to a new locationand it would be optimal for the sensor to send its data to a differentcollector. For example, it may be preferable for the sensor to send itsnetwork traffic and corresponding data on a particular path and/or to aparticular collector based on latency, shortest path, monetary cost(e.g., using private resources versus a public resources provided by apublic cloud provider), error rate, or some combination of thesefactors. In other embodiments, a sensor can send different types ofnetwork traffic and corresponding data to different collectors. Forexample, the sensor can send first network traffic and correspondingdata related to one type of process to one collector and second networktraffic and corresponding data related to another type of process toanother collector.

The collectors 106 can be any type of storage medium that can serve as arepository for the network traffic and corresponding data captured bythe sensors 104. In some embodiments, data storage for the collectors106 is located in an in-memory database, such as dashDB from IBM®,although it should be appreciated that the data storage for thecollectors 106 can be any software and/or hardware capable of providingrapid random access speeds typically used for analytics software. Invarious embodiments, the collectors 106 can utilize solid state drives,disk drives, magnetic tape drives, or a combination of the foregoingaccording to cost, responsiveness, and size requirements. Further, thecollectors 106 can utilize various database structures such as anormalized relational database or a NoSQL database, among others.

In some embodiments, the collectors 106 may only serve as networkstorage for the network traffic monitoring system 100. In suchembodiments, the network traffic monitoring system 100 can include adata mover module 108 for retrieving data from the collectors 106 andmaking the data available to network clients, such as the components ofthe analytics engine 110. In effect, the data mover module 108 can serveas a gateway for presenting network-attached storage to the networkclients. In other embodiments, the collectors 106 can perform additionalfunctions, such as organizing, summarizing, and preprocessing data. Forexample, the collectors 106 can tabulate how often packets of certainsizes or types are transmitted from different nodes of the network. Thecollectors 106 can also characterize the traffic flows going to and fromvarious nodes. In some embodiments, the collectors 106 can match packetsbased on sequence numbers, thus identifying traffic flows and connectionlinks. As it may be inefficient to retain all data indefinitely incertain circumstances, in some embodiments, the collectors 106 canperiodically replace detailed network traffic data with consolidatedsummaries. In this manner, the collectors 106 can retain a completedataset describing one period (e.g., the past minute or other suitableperiod of time), with a smaller dataset of another period (e.g., theprevious 2-10 minutes or other suitable period of time), andprogressively consolidate network traffic and corresponding data ofother periods of time (e.g., day, week, month, year, etc.). In someembodiments, network traffic and corresponding data for a set of flowsidentified as normal or routine can be winnowed at an earlier period oftime while a more complete data set may be retained for a lengthierperiod of time for another set of flows identified as anomalous or as anattack.

Computer networks may be exposed to a variety of different attacks thatexpose vulnerabilities of computer systems in order to compromise theirsecurity. Some network traffic may be associated with malicious programsor devices. The analytics engine 110 may be provided with examples ofnetwork states corresponding to an attack and network statescorresponding to normal operation. The analytics engine 110 can thenanalyze network traffic and corresponding data to recognize when thenetwork is under attack. In some embodiments, the network may operatewithin a trusted environment for a period of time so that the analyticsengine 110 can establish a baseline of normal operation. Since malwareis constantly evolving and changing, machine learning may be used todynamically update models for identifying malicious traffic patterns.

In some embodiments, the analytics engine 110 may be used to identifyobservations which differ from other examples in a dataset. For example,if a training set of example data with known outlier labels exists,supervised anomaly detection techniques may be used. Supervised anomalydetection techniques utilize data sets that have been labeled as normaland abnormal and train a classifier. In a case in which it is unknownwhether examples in the training data are outliers, unsupervised anomalytechniques may be used. Unsupervised anomaly detection techniques may beused to detect anomalies in an unlabeled test data set under theassumption that the majority of instances in the data set are normal bylooking for instances that seem to fit to the remainder of the data set.

The analytics engine 110 can include a data lake 130, an applicationdependency mapping (ADM) module 140, and elastic processing engines 150.The data lake 130 is a large-scale storage repository that providesmassive storage for various types of data, enormous processing power,and the ability to handle nearly limitless concurrent tasks or jobs. Insome embodiments, the data lake 130 is implemented using the Hadoop®Distributed File System (HDFS™) from Apache® Software Foundation ofForest Hill, Md. HDFS™ is a highly scalable and distributed file systemthat can scale to thousands of cluster nodes, millions of files, andpetabytes of data. HDFS™ is optimized for batch processing where datalocations are exposed to allow computations to take place where the dataresides. HDFS™ provides a single namespace for an entire cluster toallow for data coherency in a write-once, read-many access model. Thatis, clients can only append to existing files in the node. In HDFS™,files are separated into blocks, which are typically 64 MB in size andare replicated in multiple data nodes. Clients access data directly fromdata nodes.

In some embodiments, the data mover 108 receives raw network traffic andcorresponding data from the collectors 106 and distributes or pushes thedata to the data lake 130. The data lake 130 can also receive and storeout-of-band data 114, such as statuses on power levels, networkavailability, server performance, temperature conditions, cage doorpositions, and other data from internal sources, and third party data116, such as security reports (e.g., provided by Cisco® Systems, Inc. ofSan Jose, Calif., Arbor Networks® of Burlington, Mass., Symantec® Corp.of Sunnyvale, Calif., Sophos® Group plc of Abingdon, England, Microsoft®Corp. of Seattle, Wash., Verizon® Communications, Inc. of New York,N.Y., among others), geolocation data, IP watch lists, Whois data,configuration management database (CMDB) or configuration managementsystem (CMS) as a service, and other data from external sources. Inother embodiments, the data lake 130 may instead fetch or pull rawtraffic and corresponding data from the collectors 106 and relevant datafrom the out-of-band data sources 114 and the third party data sources116. In yet other embodiments, the functionality of the collectors 106,the data mover 108, the out-of-band data sources 114, the third partydata sources 116, and the data lake 130 can be combined. Variouscombinations and configurations are possible as would be known to one ofordinary skill in the art.

Each component of the data lake 130 can perform certain processing ofthe raw network traffic data and/or other data (e.g., host data, processdata, user data, out-of-band data or third party data) to transform theraw data to a form useable by the elastic processing engines 150. Insome embodiments, the data lake 130 can include repositories for flowattributes 132, host and/or endpoint attributes 134, process attributes136, and policy attributes 138. In some embodiments, the data lake 130can also include repositories for VM or container attributes,application attributes, tenant attributes, network topology, applicationdependency maps, cluster attributes, etc.

The flow attributes 132 relate to information about flows traversing thenetwork. A flow is generally one or more packets sharing certainattributes that are sent within a network within a specified period oftime. The flow attributes 132 can include packet header fields such as asource address (e.g., Internet Protocol (IP) address, Media AccessControl (MAC) address, Domain Name System (DNS) name, or other networkaddress), source port, destination address, destination port, protocoltype, class of service, among other fields. The source address maycorrespond to a first endpoint (e.g., network device, physical server,virtual partition, etc.) of the network, and the destination address maycorrespond to a second endpoint, a multicast group, or a broadcastdomain. The flow attributes 132 can also include aggregate packet datasuch as flow start time, flow end time, number of packets for a flow,number of bytes for a flow, the union of TCP flags for a flow, amongother flow data.

The host and/or endpoint attributes 134 describe host and/or endpointdata for each flow, and can include host and/or endpoint name, networkaddress, operating system, CPU usage, network usage, disk space, ports,logged users, scheduled jobs, open files, and information regardingfiles and/or directories stored on a host and/or endpoint (e.g.,presence, absence, or modifications of log files, configuration files,device special files, or protected electronic information). Asdiscussed, in some embodiments, the host and/or endpoints attributes 134can also include the out-of-band data 114 regarding hosts such as powerlevel, temperature, and physical location (e.g., room, row, rack, cagedoor position, etc.) or the third party data 116 such as whether a hostand/or endpoint is on an IP watch list or otherwise associated with asecurity threat, Whois data, or geocoordinates. In some embodiments, theout-of-band data 114 and the third party data 116 may be associated byprocess, user, flow, or other more granular or less granular networkelement or network communication.

The process attributes 136 relate to process data corresponding to eachflow, and can include process name (e.g., bash, httpd, netstat, etc.),ID, parent process ID, path (e.g., /usr2/username/bin/, /usr/local/bin,/usr/bin, etc.), CPU utilization, memory utilization, memory address,scheduling information, nice value, flags, priority, status, start time,terminal type, CPU time taken by the process, the command that startedthe process, and information regarding a process owner (e.g., user name,ID, user's real name, e-mail address, user's groups, terminalinformation, login time, expiration date of login, idle time, andinformation regarding files and/or directories of the user).

The policy attributes 138 contain information relating to networkpolicies. Policies establish whether a particular flow is allowed ordenied by the network as well as a specific route by which a packettraverses the network. Policies can also be used to mark packets so thatcertain kinds of traffic receive differentiated service when used incombination with queuing techniques such as those based on priority,fairness, weighted fairness, token bucket, random early detection, roundrobin, among others. The policy attributes 138 can include policystatistics such as a number of times a policy was enforced or a numberof times a policy was not enforced. The policy attributes 138 can alsoinclude associations with network traffic data. For example, flows foundto be non-conformant can be linked or tagged with corresponding policiesto assist in the investigation of non-conformance.

The analytics engine 110 may include any number of engines 150,including for example, a flow engine 152 for identifying flows (e.g.,flow engine 152) or an attacks engine 154 for identify attacks to thenetwork. In some embodiments, the analytics engine can include aseparate distributed denial of service (DDoS) attack engine 155 forspecifically detecting DDoS attacks. In other embodiments, a DDoS attackengine may be a component or a sub-engine of a general attacks engine.In some embodiments, the attacks engine 154 and/or the DDoS engine 155can use machine learning techniques to identify security threats to anetwork. For example, the attacks engine 154 and/or the DDoS engine 155can be provided with examples of network states corresponding to anattack and network states corresponding to normal operation. The attacksengine 154 and/or the DDoS engine 155 can then analyze network trafficdata to recognize when the network is under attack. In some embodiments,the network can operate within a trusted environment for a time toestablish a baseline for normal network operation for the attacks engine154 and/or the DDoS.

The analytics engine 110 may further include a search engine 156. Thesearch engine 156 may be configured, for example to perform a structuredsearch, an NLP (Natural Language Processing) search, or a visual search.Data may be provided to the engines from one or more processingcomponents.

The analytics engine 110 can also include a policy engine 158 thatmanages network policy, including creating and/or importing policies,monitoring policy conformance and non-conformance, enforcing policy,simulating changes to policy or network elements affecting policy, amongother policy-related tasks.

The ADM module 140 can determine dependencies of applications of thenetwork. That is, particular patterns of traffic may correspond to anapplication, and the interconnectivity or dependencies of theapplication can be mapped to generate a graph for the application (i.e.,an application dependency mapping). In this context, an applicationrefers to a set of networking components that provides connectivity fora given set of workloads. For example, in a three-tier architecture fora web application, first endpoints of the web tier, second endpoints ofthe application tier, and third endpoints of the data tier make up theweb application. The ADM module 140 can receive input data from variousrepositories of the data lake 130 (e.g., the flow attributes 132, thehost and/or endpoint attributes 134, the process attributes 136, etc.).The ADM module 140 may analyze the input data to determine that there isfirst traffic flowing between external endpoints on port 80 of the firstendpoints corresponding to Hypertext Transfer Protocol (HTTP) requestsand responses. The input data may also indicate second traffic betweenfirst ports of the first endpoints and second ports of the secondendpoints corresponding to application server requests and responses andthird traffic flowing between third ports of the second endpoints andfourth ports of the third endpoints corresponding to database requestsand responses. The ADM module 140 may define an ADM for the webapplication as a three-tier application including a first EPG comprisingthe first endpoints, a second EPG comprising the second endpoints, and athird EPG comprising the third endpoints.

The presentation module 112 can include an application programminginterface (API) or command line interface (CLI) 160, a securityinformation and event management (SIEM) interface 162, and a webfront-end 164. As the analytics engine 110 processes network traffic andcorresponding data and generates analytics data, the analytics data maynot be in a human-readable form or it may be too voluminous for a userto navigate. The presentation module 112 can take the analytics datagenerated by analytics engine 110 and further summarize, filter, andorganize the analytics data as well as create intuitive presentationsfor the analytics data.

In some embodiments, the API or CLI 160 can be implemented using Hadoop®Hive from Apache® for the back end, and Java® Database Connectivity(JDBC) from Oracle® Corporation of Redwood Shores, Calif., as an APIlayer. Hive is a data warehouse infrastructure that provides datasummarization and ad hoc querying. Hive provides a mechanism to querydata using a variation of structured query language (SQL) that is calledHiveQL. JDBC is an application programming interface (API) for theprogramming language Java®, which defines how a client may access adatabase.

In some embodiments, the SIEM interface 162 can be implemented usingKafka for the back end, and software provided by Splunk®, Inc. of SanFrancisco, Calif. as the SIEM platform. Kafka is a distributed messagingsystem that is partitioned and replicated. Kafka uses the concept oftopics. Topics are feeds of messages in specific categories. In someembodiments, Kafka can take raw packet captures and telemetryinformation from the data mover 108 as input, and output messages to aSIEM platform, such as Splunk®. The Splunk® platform is utilized forsearching, monitoring, and analyzing machine-generated data.

In some embodiments, the web front-end 164 can be implemented usingsoftware provided by MongoDB®, Inc. of New York, N.Y. and Hadoop®ElasticSearch from Apache® for the back-end, and Ruby on Rails™ as theweb application framework. MongoDB® is a document-oriented NoSQLdatabase based on documents in the form of JavaScript® Object Notation(JSON) with dynamic schemas. ElasticSearch is a scalable and real-timesearch and analytics engine that provides domain-specific language (DSL)full querying based on JSON. Ruby on Rails™ is model-view-controller(MVC) framework that provides default structures for a database, a webservice, and web pages. Ruby on Rails™ relies on web standards such asJSON or extensible markup language (XML) for data transfer, andhypertext markup language (HTML), cascading style sheets, (CSS), andJavaScript® for display and user interfacing.

Although FIG. 1 illustrates an example configuration of the variouscomponents of a network traffic monitoring system, those of skill in theart will understand that the components of the network trafficmonitoring system 100 or any system described herein can be configuredin a number of different ways and can include any other type and numberof components. For example, the sensors 104, the collectors 106, thedata mover 108, and the data lake 130 can belong to one hardware and/orsoftware module or multiple separate modules. Other modules can also becombined into fewer components and/or further divided into morecomponents.

FIG. 2 illustrates an example of a network environment, according to oneaspect of the present disclosure. In some embodiments, a network trafficmonitoring system, such as the network traffic monitoring system 100 ofFIG. 1, can be implemented in the network environment 200. It should beunderstood that, for the network environment 200 and any environmentdiscussed herein, there can be additional or fewer nodes, devices,links, networks, or components in similar or alternative configurations.Embodiments with different numbers and/or types of clients, networks,nodes, cloud components, servers, software components, devices, virtualor physical resources, configurations, topologies, services, appliances,deployments, or network devices are also contemplated herein. Further,the network environment 200 can include any number or type of resources,which can be accessed and utilized by clients or tenants. Theillustrations and examples provided herein are for clarity andsimplicity.

The network environment 200 can include a network fabric 202, a Layer 2(L2) network 204, a Layer 3 (L3) network 206, and servers 208 a, 208 b,208 c, 208 d, and 208 e (collectively, 208). The network fabric 202 caninclude spine switches 210 a, 210 b, 210 c, and 210 d (collectively,“210”) and leaf switches 212 a, 212 b, 212 c, 212 d, and 212 e(collectively, “212”). The spine switches 210 can connect to the leafswitches 212 in the network fabric 202. The leaf switches 212 caninclude access ports (or non-fabric ports) and fabric ports. The fabricports can provide uplinks to the spine switches 210, while the accessports can provide connectivity to endpoints (e.g., the servers 208),internal networks (e.g., the L2 network 204), or external networks(e.g., the L3 network 206).

The leaf switches 212 can reside at the edge of the network fabric 202,and can thus represent the physical network edge. For instance, in someembodiments, the leaf switches 212 d and 212 e operate as border leafswitches in communication with edge devices 214 located in the externalnetwork 206. The border leaf switches 212 d and 212 e may be used toconnect any type of external network device, service (e.g., firewall,deep packet inspector, traffic monitor, load balancer, etc.), or network(e.g., the L3 network 206) to the fabric 202.

Although the network fabric 202 is illustrated and described herein asan example leaf-spine architecture, one of ordinary skill in the artwill readily recognize that various embodiments can be implemented basedon any network topology, including any data center or cloud networkfabric. Indeed, other architectures, designs, infrastructures, andvariations are contemplated herein. For example, the principlesdisclosed herein are applicable to topologies including three-tier(including core, aggregation, and access levels), fat tree, mesh, bus,hub and spoke, etc. Thus, in some embodiments, the leaf switches 212 canbe top-of-rack switches configured according to a top-of-rackarchitecture. In other embodiments, the leaf switches 212 can beaggregation switches in any particular topology, such as end-of-row ormiddle-of-row topologies. In some embodiments, the leaf switches 212 canalso be implemented using aggregation switches.

Moreover, the topology illustrated in FIG. 2 and described herein isreadily scalable and may accommodate a large number of components, aswell as more complicated arrangements and configurations. For example,the network may include any number of fabrics 202, which may begeographically dispersed or located in the same geographic area. Thus,network nodes may be used in any suitable network topology, which mayinclude any number of servers, virtual machines or containers, switches,routers, appliances, controllers, gateways, or other nodesinterconnected to form a large and complex network. Nodes may be coupledto other nodes or networks through one or more interfaces employing anysuitable wired or wireless connection, which provides a viable pathwayfor electronic communications.

Network communications in the network fabric 202 can flow through theleaf switches 212. In some embodiments, the leaf switches 212 canprovide endpoints (e.g., the servers 208), internal networks (e.g., theL2 network 204), or external networks (e.g., the L3 network 206) accessto the network fabric 202, and can connect the leaf switches 212 to eachother. In some embodiments, the leaf switches 212 can connect endpointgroups (EPGs) to the network fabric 202, internal networks (e.g., the L2network 204), and/or any external networks (e.g., the L3 network 206).EPGs are groupings of applications, or application components, and tiersfor implementing forwarding and policy logic. EPGs can allow forseparation of network policy, security, and forwarding from addressingby using logical application boundaries. EPGs can be used in the networkenvironment 200 for mapping applications in the network. For example,EPGs can comprise a grouping of endpoints in the network indicatingconnectivity and policy for applications.

As discussed, the servers 208 can connect to the network fabric 202 viathe leaf switches 212. For example, the servers 208 a and 208 b canconnect directly to the leaf switches 212 a and 212 b, which can connectthe servers 208 a and 208 b to the network fabric 202 and/or any of theother leaf switches. The servers 208 c and 208 d can connect to the leafswitches 212 b and 212 c via the L2 network 204. The servers 208 c and208 d and the L2 network 204 make up a local area network (LAN). LANscan connect nodes over dedicated private communications links located inthe same general physical location, such as a building or campus.

The WAN 206 can connect to the leaf switches 212 d or 212 e via the L3network 206. WANs can connect geographically dispersed nodes overlong-distance communications links, such as common carrier telephonelines, optical light paths, synchronous optical networks (SONET), orsynchronous digital hierarchy (SDH) links. LANs and WANs can include L2and/or L3 networks and endpoints.

The Internet is an example of a WAN that connects disparate networksthroughout the world, providing global communication between nodes onvarious networks. The nodes typically communicate over the network byexchanging discrete frames or packets of data according to predefinedprotocols, such as the Transmission Control Protocol/Internet Protocol(TCP/IP). In this context, a protocol can refer to a set of rulesdefining how the nodes interact with each other. Computer networks maybe further interconnected by an intermediate network node, such as arouter, to extend the effective size of each network. The endpoints 208can include any communication device or component, such as a computer,server, blade, hypervisor, virtual machine, container, process (e.g.,running on a virtual machine), switch, router, gateway, host, device,external network, etc.

In some embodiments, the network environment 200 also includes a networkcontroller running on the host 208 a. The network controller isimplemented using the Application Policy Infrastructure Controller(APIC™) from Cisco®. The APIC™ provides a centralized point ofautomation and management, policy programming, application deployment,and health monitoring for the fabric 202. In some embodiments, the APIC™is operated as a replicated synchronized clustered controller. In otherembodiments, other configurations or software-defined networking (SDN)platforms can be utilized for managing the fabric 202.

In some embodiments, a physical server 208 may have instantiated thereona hypervisor 216 for creating and running one or more virtual switches(not shown) and one or more virtual machines 218, as shown for the host208 b. In other embodiments, physical servers may run a shared kernelfor hosting containers. In yet other embodiments, the physical server208 can run other software for supporting other virtual partitioningapproaches. Networks in accordance with various embodiments may includeany number of physical servers hosting any number of virtual machines,containers, or other virtual partitions. Hosts may also compriseblade/physical servers without virtual machines, containers, or othervirtual partitions, such as the servers 208 a, 208 c, 208 d, and 208 e.

The network environment 200 can also integrate a network trafficmonitoring system, such as the network traffic monitoring system 100shown in FIG. 1. For example, the network traffic monitoring system ofFIG. 2 includes sensors 220 a, 220 b, 220 c, and 220 d (collectively,“220”), collectors 222, and an analytics engine, such as the analyticsengine 110 of FIG. 1, executing on the server 208 e. The analyticsengine 208 e can receive and process network traffic data collected bythe collectors 222 and detected by the sensors 220 placed on nodeslocated throughout the network environment 200. Although the analyticsengine 208 e is shown to be a standalone network appliance in FIG. 2, itwill be appreciated that the analytics engine 208 e can also beimplemented as a virtual partition (e.g., VM or container) that can bedistributed onto a host or cluster of hosts, software as a service(SaaS), or other suitable method of distribution. In some embodiments,the sensors 220 run on the leaf switches 212 (e.g., the sensor 220 a),the hosts 208 (e.g., the sensor 220 b), the hypervisor 216 (e.g., thesensor 220 c), and the VMs 218 (e.g., the sensor 220 d). In otherembodiments, the sensors 220 can also run on the spine switches 210,virtual switches, service appliances (e.g., firewall, deep packetinspector, traffic monitor, load balancer, etc.) and in between networkelements. In some embodiments, sensors 220 can be located at each (ornearly every) network component to capture granular packet statisticsand data at each hop of data transmission. In other embodiments, thesensors 220 may not be installed in all components or portions of thenetwork (e.g., shared hosting environment in which customers haveexclusive control of some virtual machines).

As shown in FIG. 2, a host may include multiple sensors 220 running onthe host (e.g., the host sensor 220 b) and various components of thehost (e.g., the hypervisor sensor 220 c and the VM sensor 220 d) so thatall (or substantially all) packets traversing the network environment200 may be monitored. For example, if one of the VMs 218 running on thehost 208 b receives a first packet from the WAN 206, the first packetmay pass through the border leaf switch 212 d, the spine switch 210 b,the leaf switch 212 b, the host 208 b, the hypervisor 216, and the VM.Since all or nearly all of these components contain a respective sensor,the first packet will likely be identified and reported to one of thecollectors 222. As another example, if a second packet is transmittedfrom one of the VMs 218 running on the host 208 b to the host 208 d,sensors installed along the data path, such as at the VM 218, thehypervisor 216, the host 208 b, the leaf switch 212 b, and the host 208d will likely result in capture of metadata from the second packet.

FIG. 3 illustrates an example of a data pipeline for generating networkinsights based on collected network information, according to one aspectof the present disclosure. The insights generated from data pipeline 300may include, for example, discovered applications or inventories,application dependencies, policies, efficiencies, resource and bandwidthusage, network flows and status of devices and/or associated usershaving access to the network can be determined for the network using thenetwork traffic data. In some embodiments, the data pipeline 300 can bedirected by a network traffic monitoring system, such as the networktraffic monitoring system 100 of FIG. 1; an analytics engine, such asthe analytics engine 110 of FIG. 1; or other network service or networkappliance. For example, an analytics engine 110 can be configured todiscover applications running in the network, map the applications'interdependencies, generate a set of proposed network policies forimplementation, and monitor policy conformance and non-conformance amongother network-related tasks.

The data pipeline 300 includes a data collection stage 302 in whichnetwork traffic data and corresponding data (e.g., host data, processdata, user data, etc.) are captured by sensors (e.g., the sensors 104 ofFIG. 1) located throughout the network. The data may comprise, forexample, raw flow data and raw process data. As discussed, the data canbe captured from multiple perspectives to provide a comprehensive viewof the network. The data collected may also include other types ofinformation, such as tenant information, virtual partition information,out-of-band information, third party information, and other relevantinformation. In some embodiments, the flow data and associated data canbe aggregated and summarized daily or according to another suitableincrement of time, and flow vectors, process vectors, host vectors, andother feature vectors can be calculated during the data collection stage302. This can substantially reduce processing.

The data pipeline 300 may also include an input data stage 304 in whicha network or security administrator or other authorized user mayconfigure insight generation by selecting the date range of the flowdata and associated data to analyze, and those nodes for which theadministrator wants to analyze. In some embodiments, the administratorcan also input side information, such as server load balance, routetags, and previously identified clusters during the input data stage304. In other embodiments, the side information can be automaticallypulled or another network element can push the side information.

The next stage of the data pipeline 300 is pre-processing 306. Duringthe pre-processing stage 306, nodes of the network are partitioned intoselected node and dependency node subnets. Selected nodes are thosenodes for which the user requests application dependency maps andcluster information. Dependency nodes are those nodes that are notexplicitly selected by the users for an ADM run but are nodes thatcommunicate with the selected nodes. To obtain the partitioninginformation, edges of an application dependency map (i.e., flow data)and unprocessed feature vectors can be analyzed.

Other tasks can also be performed during the pre-processing stage 306,including identifying dependencies of the selected nodes and thedependency nodes; replacing the dependency nodes with tags based on thedependency nodes' subnet names; extracting feature vectors for theselected nodes, such as by aggregating daily vectors across multipledays, calculating term frequency-inverse document frequency (tf-idf),and normalizing the vectors (e.g., l₂ normalization); and identifyingexisting clusters.

In some embodiments, the pre-processing stage 306 can include earlyfeature fusion pre-processing. Early fusion is a fusion scheme in whichfeatures are combined into a single representation. Features may bederived from various domains (e.g., network, host, virtual partition,process, user, etc.), and a feature vector in an early fusion system mayrepresent the concatenation of disparate feature types or domains.

Early fusion may be effective for features that are similar or have asimilar structure (e.g., fields of TCP and UDP packets or flows). Suchfeatures may be characterized as being a same type or being within asame domain. Early fusion may be less effective for distant features orfeatures of different types or domains (e.g., flow-based features versusprocess-based features). Thus, in some embodiments, only features in thenetwork domain (i.e., network traffic-based features, such as packetheader information, number of packets for a flow, number of bytes for aflow, and similar data) may be analyzed. In other embodiments, analysismay be limited to features in the process domain (i.e., process-basedfeatures, such as process name, parent process, process owner, etc.). Inyet other embodiments, feature sets in other domains (e.g., the hostdomain, virtual partition domain, user domain, etc.) may be the.

After pre-processing, the data pipeline 300 may proceed to an insightgeneration stage 308. During the insight generation stage 308, the datacollected and inputted into the data pipeline 300 may be used togenerate various network insights. For example, an analytics engine 110can be configured to discover of applications running in the network,map the applications' interdependencies, generate a set of proposednetwork policies for implementation, and monitor policy conformance andnon-conformance among other network-related tasks. Various machinelearning techniques can be implemented to analyze feature vectors withina single domain or across different domains to generate insights.Machine learning is an area of computer science in which the goal is todevelop models using example observations (i.e., training data), thatcan be used to make predictions on new observations. The models or logicare not based on theory but are empirically based or data-driven.

After clusters are identified, the data pipeline 300 can include apost-processing stage 310. The post-processing stage 310 can includetasks such as filtering insight data, converting the insight data into aconsumable format, or any other preparations needed to prepare theinsight data for consumption by an end user. At the output stage 312,the generated insights may be provided to an end user. The end user maybe, for example a network administrator, a third-party computing system,a computing system in the network, or any other entity configured toreceive the insight data. In some cases, the insight data may beconfigured to be displayed on a screen or provided to a system forfurther processing, consumption, or storage.

With examples of network traffic monitoring systems, their operationsand network environments in which they can be deployed described above,the disclosure now turns to FIGS. 4-7, which describe methods andexamples for measuring effectiveness of segmentation policiesimplemented within an enterprise network such as network environment 200of FIG. 2 using tools and functionalities of network monitoring system100 of FIG. 1.

As noted above, the present disclosure is directed to providing anobjective and standardized system and approach for measuring theeffectiveness of a derived or enforced network segmentation policies.Segmentation effectiveness may be defined as a measure of effectivenessof a particular policy given the exposure of an application to hostswithin the infrastructure of an enterprise network and/or externalsources accessible via the enterprise network (e.g., an externalinternet host). Such measurement may be indicative of how well a givenpolicy is satisfying the determined objective of preventing unauthorizedtraffic or hosts from accessing an application or a set ofservices/workloads executed in association with an application. Thisobjective measurement improves efficiency in ensuring network securityby allowing policies to be measured before implementation and adjustedif need be and/or to reliably adjust existing and currently implementednetwork segmentation policies in the network.

As will be described below, effectiveness score may be determined basedon a number of variables such as source exposure risk, destinationexposure risk and security posture, as defined above.

FIG. 4 describes a process of determining an effectiveness of asegmentation policy, according to one aspect of the present disclosure.FIG. 4 will be described from the perspective of analytics engine 150 ofnetwork monitoring system 100, executing computer-readable instructionscorresponding to ADM module (service) 140. FIG. 4 will be described withreference to FIGS. 1-3.

In order to implement the process of FIG. 4, software agents such assensors 104 are deployed to various nodes and workloads in networkenvironment 200. For example such sensors may be deployed to bare metalor a virtual machine (examples of network nodes/hosts) and/or workloadssuch as Windows or Linux.

At S400, analytics engine 150 may receive a request via, for example,API 160 for determining segmentation policy effectiveness.Alternatively, analytics engine 150 may periodically perform suchsegmentation policy effectiveness analysis, with periodicity being aconfigurable parameter determined based on experiments and/or empiricalstudies (e.g., once every minute, once every few hours, once every day,once every week, once every month, once every year, etc.).

At S402, analytics engine 150 determines an application hierarchy usingtelemetry data collected by sensors 104. Such collected telemetry datamay then be annotated to provide contextual information for thecollected flows and telemetry data and may include information such asindividual IP addresses or subnets in a time series data base along withassociated department within an organization (e.g., human resources, ITdepartment, etc.) as well as one or more datacenters in which aparticular workload is being executed.

An example of network monitoring system 100 may be Tetration platformdeveloped by Cisco Inc., of San Jose, Calif. In the non-limiting exampleof Tetration platform, a process by which such application hierarchy maybe determined is referred to as Scopes. Scopes may be used to identifyapplication memberships, which is then used in turn within applicationsegmentation to control application visibility and granularity. Scopemembership may identify information such as role based access for eachapplication as well as identifying of children or parent applications,etc.

At S404, analytics engine 150 may determine/define applicationmemberships to identify all workloads that make up an application using,for example, Scopes process of Tetration described above.

At S406, analytics engine 150 may execute ADM logic 140 to implementapplication dependency mapping (segmentation) that provides insight intoapplications running in a datacenter or in a multi-datacenterenvironment or enterprise network. Application dependency mapping mayidentify information including, but not limited to, services, providersand consumers as well as rules defined by services. Workloads associatedwith each provider and consumer may also be determined throughapplication dependency mapping. At S406 and by implementing applicationdependency mapping, policies (segmentation policies) for all applicationdependencies may be identified (if already implemented or enforced) orsuggested (for implementation and enforcement).

At S408, analytics engine 150 determines a Segmentation PolicyEffectiveness score or simply Policy Effectiveness Score (PES) for eachpolicy identified or suggested at S406 through application dependencymapping. The process of determining PES for each policy will bedescribed in detail with respect to FIG. 5. A PES may be accompaniedwith further information such as a corresponding application's detailsthrough the application's segmentation journey as the application movesfrom a non-segmented state in incremental steps towards a zero truststate in the network environment 200.

At S410, all PESs (for all identified/suggested policies) are evaluatedto determine if a particular policy should be modified, replaced ormaintained (enforced using current version). Results of PESs may bepresented on a GUI in a tabular form as will be described below withreference to FIG. 7.

Determination on modification/replacement or maintaining (execution) ofpolicies may be made in a variety of different ways. For example, PESscores and associated details may be presented on a GUI for manualreview by a network operator and a determination of whether it should bemodified, replaced or maintained. In another example, a machine-learningalgorithm or model may be deployed that may, over time, be trained toidentify correlations between how a policy may be modified, replaced ormaintained given a particular PES score. Example considerations formodifying, replacing and/or maintaining a given policy include, but arenot limited to, application behavior given a particular policy,consumer/provider cluster sizes, number of workloads, etc.

If at S410, analytics engine 150 determines that a particular policy isto be adjusted or modified, then at S412 the policy is adjusted/modified(executed) according to any known or to be developed approach ormethodology in the industry. Otherwise, the process reverts back to S404and S404, S406, S408, S410 and S412 may be repeated.

At S414, information associated with determining PES and correspondingmodification/replacement/maintenance (execution) of policies may bestored in database of network monitoring system 100 for future referenceand/or training of analytics engine 150 for further autonomous PESdetermination and modification, replacement and maintaining of networkpolicies as described above.

FIG. 5 describes a process for determining policy effectiveness score ofFIG. 4, according to one aspect of the present disclosure. FIG. 5 willbe described from the perspective of analytics engine 150 of networkmonitoring system 100, executing computer-readable instructionscorresponding to ADM module (service) 140. FIG. 4 will be described withreference to FIGS. 1-4.

In describing FIG. 5, an assumption is made that a policy isidentified/suggested by analytics engine 150 at S406.

At S500, analytics engine 150 identifies a data set for a givenidentified/suggested policy (policy data set) from, for example, apolicy recommendation engine such as policy engine 138 of FIG. 1.Examples of a policy recommendation engine include, but are not limitedto, policy recommendation engine of Tetration platform developed byCisco, Inc. of San Jose, Calif., Illumio, Guardicore, etc.

At S502, analytics engine 150 identifies policy consumers (clients) forthe given policy. A policy consumer may be defined as aninitiator/originator of network traffic. For example, a user browsing toa web page would be a non-limiting example of a policy consumer.

At S504, analytics engine 150 identifies a total number of providers(servers) for the given policy. A provider may be defined as adestination providing a network service. For example, when a userbrowses to a web page, the host providing web services for the webpagewould be a non-limiting example of a provider.

At S506, analytics engine 150 identifies policy services (providerports) that form/constitute the given policy. Services may be associatedwith ports and protocols required to access a given application. In anon-limiting example of a user accessing a web site, policy serviceswould typically be TCP PORTs 80 and 443.

FIG. 6, illustrates an example policy and associated policy consumers,providers and services. FIG. 6 will be further described below.

At S508, analytics engine 150 determines a segmentation policyeffectiveness score (PES) for the given policy based on the identifiedpolicy consumers, providers and services. In one example, PES may bedetermined according to the following formula:PES=1−source-risk/potential source-risk  (1)

In formula (1), source-risk is the total number of consumers, providersand policy services identified per S502, S504 and S506. Potentialsource-risk is a total number of consumers, providers and policyservices that can potentially access an application if there is nosecurity or segmentation policy (total number of consumers, providersand policy services known to the monitoring system of FIG. 1).

Per formula (1), PES score may range from 0 to 1. A Score of 1 may beindicative of a completely effective segmentation policy while a scoreof 0 may be indicative of a completely ineffective segmentation policy.Scale of PESs may not be limited to 0 to 1 and may be any other range(e.g., 0-10, 0-20, 0-100, etc.).

At S510, the process returns to S408 of FIG. 4 and the process of FIG. 4is continued as described above.

By implementing the process of FIGS. 4-7, an objective measurement ofeffectiveness of network segmentation policies may be achieved, whichadvantageously allows for such policies to be measured beforeimplementation and adjusted if need be and/or to reliably adjustexisting and currently implemented network segmentation policies in thenetwork.

With a process of determining an effectiveness of a segmentation policy,described with reference to FIGS. 4 and 5, FIG. 6 is described next,which is an example segmentation policy, mentioned above.

FIG. 6 is an example network segmentation policy, according to an aspectof the present disclosure. As can be seen from example policy 600 ofFIG. 6, analytics engine 150 is able to identify services provided(e.g., port 443), number of policy consumers (e.g., 2.0) and number ofpolicy providers (17.0), which is then used per formula 1 to determinePES.

FIG. 7 illustrates a result of determining policy effectiveness score ina tabular form, according to one aspect of the present disclosure. Table700 is a non-limiting example where effectiveness of a policy is shownfor 3 applications 702. For each application from applications 702,associated potential source risk 704, source risk 706 and consumers 708are identified. Finally, using formula (1) a corresponding PES 710 iscalculated when the policy is applied to each of the applications 702.

With examples of PES determination described with reference to FIGS.4-7, the disclosure now turns to FIG. 8, which illustrates an examplecomputing system, according to one aspect of the present disclosure.Such example computing system can be used to implement analytics engine150 and/or any other component of network monitoring system 100 of FIG.1 and/or network environment 200 of FIG. 2.

FIG. 8 shows an example of computing system 800, which can be forexample any computing device making up authentication service 415 or anycomponent thereof in which the components of the system are incommunication with each other using connection 805. Connection 805 canbe a physical connection via a bus, or a direct connection intoprocessor 810, such as in a chipset architecture. Connection 805 canalso be a virtual connection, networked connection, or logicalconnection.

In some embodiments computing system 800 is a distributed system inwhich the functions described in this disclosure can be distributedwithin a datacenter, multiple datacenters, a peer network, etc. In someembodiments, one or more of the described system components representsmany such components each performing some or all of the function forwhich the component is described. In some embodiments, the componentscan be physical or virtual devices.

Example computing system 800 includes at least one processing unit (CPUor processor) 810 and connection 805 that couples various systemcomponents including system memory 815, such as read only memory (ROM)820 and random access memory (RAM) 825 to processor 810. Computingsystem 800 can include a cache of high-speed memory 812 connecteddirectly with, in close proximity to, or integrated as part of processor810.

Processor 810 can include any general purpose processor and a hardwareservice or software service, such as services 832, 834, and 836 storedin storage device 830, configured to control processor 810 as well as aspecial-purpose processor where software instructions are incorporatedinto the actual processor design. Processor 810 may essentially be acompletely self-contained computing system, containing multiple cores orprocessors, a bus, memory controller, cache, etc. A multi-core processormay be symmetric or asymmetric.

To enable user interaction, computing system 800 includes an inputdevice 845, which can represent any number of input mechanisms, such asa microphone for speech, a touch-sensitive screen for gesture orgraphical input, keyboard, mouse, motion input, speech, etc. Computingsystem 800 can also include output device 835, which can be one or moreof a number of output mechanisms known to those of skill in the art. Insome instances, multimodal systems can enable a user to provide multipletypes of input/output to communicate with computing system 800.Computing system 800 can include communications interface 840, which cangenerally govern and manage the user input and system output. There isno restriction on operating on any particular hardware arrangement andtherefore the basic features here may easily be substituted for improvedhardware or firmware arrangements as they are developed.

Storage device 830 can be a non-volatile memory device and can be a harddisk or other types of computer readable media which can store data thatare accessible by a computer, such as magnetic cassettes, flash memorycards, solid state memory devices, digital versatile disks, cartridges,random access memories (RAMs), read only memory (ROM), and/or somecombination of these devices.

The storage device 830 can include software services, servers, services,etc., that when the code that defines such software is executed by theprocessor 810, it causes the system to perform a function. In someembodiments, a hardware service that performs a particular function caninclude the software component stored in a computer-readable medium inconnection with the necessary hardware components, such as processor810, connection 805, output device 835, etc., to carry out the function.

For clarity of explanation, in some instances the various embodimentsmay be presented as including individual functional blocks includingfunctional blocks comprising devices, device components, steps orroutines in a method embodied in software, or combinations of hardwareand software.

Claim language reciting “at least one of” refers to at least one of aset and indicates that one member of the set or multiple members of theset satisfy the claim. For example, claim language reciting “at leastone of A and B” means A, B, or A and B.

In some embodiments the computer-readable storage devices, mediums, andmemories can include a cable or wireless signal containing a bit streamand the like. However, when mentioned, non-transitory computer-readablestorage media expressly exclude media such as energy, carrier signals,electromagnetic waves, and signals per se.

Methods according to the above-described examples can be implementedusing computer-executable instructions that are stored or otherwiseavailable from computer readable media. Such instructions can comprise,for example, instructions and data which cause or otherwise configure ageneral purpose computer, special purpose computer, or special purposeprocessing device to perform a certain function or group of functions.Portions of computer resources used can be accessible over a network.The computer executable instructions may be, for example, binaries,intermediate format instructions such as assembly language, firmware, orsource code. Examples of computer-readable media that may be used tostore instructions, information used, and/or information created duringmethods according to described examples include magnetic or opticaldisks, flash memory, USB devices provided with non-volatile memory,networked storage devices, and so on.

Devices implementing methods according to these disclosures can comprisehardware, firmware, and/or software, and can take any of a variety ofform factors. Typical examples of such form factors include laptops,smart phones, small form factor personal computers, personal digitalassistants, rackmount devices, standalone devices, and so on.Functionality described herein also can be embodied in peripherals oradd-in cards. Such functionality can also be implemented on a circuitboard among different chips or different processes executing in a singledevice, by way of further example.

The instructions, media for conveying such instructions, computingresources for executing them, and other structures for supporting suchcomputing resources are means for providing the functions described inthese disclosures.

Although a variety of examples and other information was used to explainaspects within the scope of the appended claims, no limitation of theclaims should be implied based on particular features or arrangements insuch examples, as one of ordinary skill would be able to use theseexamples to derive a wide variety of implementations. Further andalthough some subject matter may have been described in languagespecific to examples of structural features and/or method steps, it isto be understood that the subject matter defined in the appended claimsis not necessarily limited to these described features or acts. Forexample, such functionality can be distributed differently or performedin components other than those identified herein. Rather, the describedfeatures and steps are disclosed as examples of components of systemsand methods within the scope of the appended claims.

The invention claimed is:
 1. A method comprising: identifying one ormore applications within an enterprise network; identifying at least onenetwork security policy in association with the one or more applicationswithin the enterprise network; determining a score of the networksecurity policy based on information corresponding to exposure of eachof the one or more applications within the enterprise network, the scorerepresenting how well the network security policy is preventingunauthorized traffic or hosts from accessing the one or moreapplications or a set of services/workloads executed in association withthe one or more applications, exposure of each of the one or moreapplications being based on: a source exposure risk indicative of atotal number of hosts and workloads accessing a particular application;a destination exposure risk indicative of a total number of hosts andworkloads providing services for the particular application; a securityposture based on overall consumers and providers for the particularapplication; and executing the network security policy based on thescore.
 2. The method of claim 1, further comprising: receiving a requestfor determining network security policy scores.
 3. The method of claim1, wherein identifying the one or more applications comprises:determining an application hierarchy and membership for each of the oneor more applications in the enterprise network.
 4. The method of claim1, wherein identifying the network security policy comprises: performingan application dependency mapping for the one or more applications. 5.The method of claim 1, wherein determining the score for the networksecurity policy comprises: identifying the information corresponding tothe exposure of the one or more applications.
 6. The method of claim 5,wherein the information includes providers, consumers and servicesassociated with each of the one or more applications.
 7. The method ofclaim 1, wherein executing the network security policy includes one ofmodifying, replacing or maintaining a current version of the networksecurity policy.
 8. A device comprising: one or more processors; and anon-transitory computer-readable storage medium comprising instructionsstored thereon which, when executed by the one or more processors, causethe one or more processors to: identify one or more applications withinan enterprise network; identify at least one network security policy inassociation with the one or more applications within the enterprisenetwork; determine a score of the network security policy based oninformation corresponding to exposure of each of the one or moreapplications within the enterprise network, the score representing howwell the network security policy is preventing unauthorized traffic orhosts from accessing the one or more applications or a set ofservices/workloads executed in association with the one or moreapplications, exposure of each of the one or more applications beingbased on: a source exposure risk indicative of a total number of hostsand workloads accessing a particular application; a destination exposurerisk indicative of a total number of hosts and workloads providingservices for the particular application; a security posture based onoverall consumers and providers for the particular application; andexecute the network security policy based on the score.
 9. The device ofclaim 8, wherein the one or more processors are configured to executethe computer-readable instructions to receive a request for determiningnetwork security policy scores.
 10. The device of claim 8, wherein theone or more processors are configured to execute the computer-readableinstructions to determine an application hierarchy and membership foreach of the one or more applications in the enterprise network.
 11. Thedevice of claim 8, wherein the one or more processors are configured toexecute the computer-readable instructions to determine the score forthe network security policy by performing an application dependencymapping for the one or more applications.
 12. The device of claim 8,wherein the one or more processors are configured to execute thecomputer-readable instructions to identify the information correspondingto the exposure of the one or more applications for determining thescore.
 13. The device of claim 12, wherein the information includesproviders, consumers and services associated with each of the one ormore applications.
 14. The device of claim 8, wherein the one or moreprocessors are configured to execute the computer-readable instructionsto execute the network security policy by one of modifying, replacing ormaintaining a current version of the network security policy.
 15. Anon-transitory computer-readable storage medium comprisingcomputer-readable instructions stored thereon which, when executed byone or more processors, cause the one or more processors to: identifyone or more applications within an enterprise network; identify at leastone network security policy in association with the one or moreapplications within the enterprise network; determine a score of thenetwork security policy based on information corresponding to exposureof each of the one or more applications within the enterprise network,the score representing how well the network security policy ispreventing unauthorized traffic or hosts from accessing the one or moreapplications or a set of services/workloads executed in association withthe one or more applications, exposure of each of the one or moreapplications being based on: a source exposure risk indicative of atotal number of hosts and workloads accessing a particular application;a destination exposure risk indicative of a total number of hosts andworkloads providing services for the particular application; a securityposture based on overall consumers and providers for the particularapplication; and execute the network security policy based on the score.16. The non-transitory computer-readable storage medium of claim 15,wherein the execution of the computer-readable instructions cause theone or more processors to receive a request for determining networksecurity policy scores.
 17. The non-transitory computer-readable storagemedium of claim 15, wherein the execution of the computer-readableinstructions cause the one or more processors to determine anapplication hierarchy and membership for each of the one or moreapplications in the enterprise network.
 18. The non-transitorycomputer-readable storage medium of claim 15, wherein the execution ofthe computer-readable instructions cause the one or more processors todetermine the score for the network security policy by performing anapplication dependency mapping for the one or more applications.
 19. Thenon-transitory computer-readable storage medium of claim 15, wherein theexecution of the computer-readable instructions cause the one or moreprocessors to identify the information corresponding to the exposure ofthe one or more applications for determining the score, the informationincluding providers, consumers and services associated with each of theone or more applications.
 20. The non-transitory computer-readablestorage medium of claim 15, wherein the execution of thecomputer-readable instructions cause the one or more processors toexecute the network security policy by one of modifying, replacing ormaintaining a current version of the network security policy.